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Sprint projectOct 26, 2026Waterloo Canada

Undermining Coalitions: The Impact of Social Manipulation in Multi-Agent Games

Zhuoran Lyu · Team Colluding AI

Submitted to AI Collusion Research Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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This project investigates whether targeted social interventions can disrupt established coalitions among large language model (LLM) agents. Using a modified version of Liar's Bar, we evaluated four strategies (private disclosure, conditional threats, public exposure, and deceptive provocation) against a no-intervention control. DeepSeek V4.1 Flash and Llama 3.1 8B were tested across 300 games.

Control games demonstrated substantial favoritism toward coalition partners. Although certain interventions increased individual challenges against partners, only warnings produced a statistically significant effect on DeepSeek's challenges against all partner plays after multiple-comparison correction. No intervention significantly reduced overall coalition favoritism. Private interventions also increased internal discussions about the undercover agent.

These findings suggest that communication-based interventions may influence specific agent behaviors without reliably disrupting coalition-wide favoritism, highlighting the challenges of mitigating collusion after coalition formation.

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Cite this project

@misc{lyu2026undermining,
  title = {{Undermining Coalitions: The Impact of Social Manipulation in Multi-Agent Games}},
  author = {Zhuoran Lyu},
  year = {2026},
  month = oct,
  note = {Submitted to AI Collusion Research Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/undermining-coalitions-the-impact-of-social-manipulation-in-multiagent-games-bhfa}},
  url = {https://apartresearch.com/sprints/projects/undermining-coalitions-the-impact-of-social-manipulation-in-multiagent-games-bhfa}
}

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